Senior Fraud Modeling & Data Scientist

Citi; San Antonio (TX); On-site; USD 97,000 - 145,000; Full time

Job summary

Citi seeks a skilled data scientist to advance fraud prevention through ML and feature engineering. You will transform complex data into effective inputs for high-performance fraud models and work across the full fraud lifecycle—from application fraud to evolving attack methods.

The role demands a strong technical background in data processing, model optimization, and collaboration with tech teams and business partners. Expect responsibilities in real-time analytics and scalable fraud solutions.

Qualifications

• Bachelor’s Degree required in statistics, mathematics, physics, economics, or other analytical discipline.
• 3+ years in data science, machine learning, or advanced analytics.
• Proficiency in Python, R, or SQL for data manipulation, feature engineering, and model development.

Responsibilities

• Lead data and feature engineering to extract high-quality inputs for fraud model development.
• Build predictive models and ML/AI algorithms with large structured and unstructured data.
• Design, develop, and implement advanced ML models to detect and prevent fraud across the lifecycle.
• Utilize data processing techniques to manage large datasets, including cleaning and normalization.
• Conduct exploratory data analysis to uncover patterns for model development.
• Collaborate with technology teams, analytics, and business partners on data strategies.
• Continuously optimize models via feature selection and hyperparameter tuning.
• Support model deployment and real-time analytics in production systems.

Skills

Python

R

SQL

Pandas

Numpy

Spark

Feature engineering

Fraud detection

Education

Bachelor's Degree

Master's Degree preferred

PhD preferred

Tools

PySpark

TensorFlow

Job description

Citi seeks a skilled data scientist to advance fraud prevention through ML and feature engineering. You will transform complex data into effective inputs for high-performance fraud models and work across the full fraud lifecycle—from application fraud to evolving attack methods.

The role demands a strong technical background in data processing, model optimization, and collaboration with tech teams and business partners. Expect responsibilities in real-time analytics and scalable fraud solutions.